Guanchen Zong
Papers
4
Total Citations
55
H-Index
3
About
Guanchen Zong is a researcher at the forefront of robotic manufacturing and rehabilitation engineering, whose work bridges intelligent control, digital twin simulation, and advanced welding technologies. His primary research areas include robotic friction stir welding (RFSW), trajectory optimization for rehabilitation robotics, and vibration mitigation in industrial processes. Zong’s major contributions are twofold: he pioneered a Back Propagation neural network optimized by a Genetic Algorithm for planning upper-limb rehabilitation trajectories, demonstrating how AI can enhance patient-specific robotic therapy—a study that has garnered 27 citations. In parallel, he has advanced RFSW by developing a digital twin virtual welding approach that co-simulates finite element models with robotic models to correct end-effector deviations caused by low robot stiffness (19 citations). His work on optimizing installation positions for complex weldments using dynamic dual particle swarm optimization (7 citations) and on active vibration avoidance through constant heat input control (2 citations) further underscores his impact. Zong’s research is notable for its practical applications in aerospace and new energy vehicles, where precision and reliability are critical. His integration of machine learning, simulation, and real-time control marks him as an innovator in smart manufacturing and assistive robotics.
Research Focus
Key Achievements
Top Papers
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